The increase in electronic commerce over the Internet has resulted in a growing demand for websites to track their online customers' behavior and activity while at their sites. Tracking this activity enables the websites to better understand their customers, which provides insight into ways in which the websites' service and/or offerings can be improved. Websites can track this information on their own, but larger sites enlist the aid of third party application software or a third party application service provider (“ASP”) to do the work for them.
Tracking customer activity generally entails storing event-level data to a log file. Event-level data represents specific events that describe a customer's presence and/or activity at a website, such as clicking on a specific web page or buying a specific product. After a certain period of time, an ASP, for instance, may analyze the event-level data in the log file according to desired metrics (e.g., total revenue, top requested web pages, etc.) and the results are provided to the client website in the form of a report. Some web-based ASPs provide this analysis to the client via interactive reports accessible through the client's web browser. The interactive element of the report allows the client to view a desired analysis by altering the report parameters in real time.
A major drawback to this process is the cost associated with the processing, storage and maintenance of the log files, which can be quite large for client websites with high traffic volume. Each time a client requests a particular analysis of the event-level data through their web browser interface, the ASP has to perform the requested analysis on the entire set of data in the log file, most of which is not relevant to the requested analysis.
Some ASPs have attempted to control this cost by reducing the size of the log file before analysis is fully performed on the data. Such reduction typically involves a simple deletion of all data not associated with a particular metric, such as the top web pages visited or the top products sold on the site. Although this may reduce the size of the log file somewhat, it discards data that may be relevant to a second and separate analysis requested by the client. And this data reduction implementation does not address the expensive cost in processing time associated with performing each analysis on the entire set of event-level data, even if the size of the log file is somewhat reduced.
Accordingly, there is a need in the art for a system and method for cost-effective and efficient analysis of online customer activity and behavior at a website without sacrificing information relevant to the analysis.